Raman Plus X: Biomedical Applications of Multimodal Raman Spectroscopy

Das, Nandan; Dai, Yichuan; Liu, Peng; Hu, Chuanzhen; Tong, Lieshu; Chen, Xiaoya; Smith, Zachary J.

doi:10.3390/s17071592

Cited by 33 publications

(10 citation statements)

References 133 publications

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“…Integration of Raman spectroscopy with other modalities can lead to significantly improved performance of a combined multimodal measurement in comparison to what either modality could achieve alone. 78 Raman spectroscopy and its derivations are valuable for a range of clinical applications in the field of medicine including prostate cancer. 28 To date, few studies have investigated the value in combining Raman Chemical Imaging (RCI) with other modalities.…”

Section: Discussionmentioning

confidence: 99%

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

Doherty,

McKeever,

Al-Attar

et al. 2021

Preprint

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The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP -RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.

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Section: Discussionmentioning

confidence: 99%

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

Doherty,

McKeever,

Al-Attar

et al. 2021

Preprint

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“…Bimodal fluorescence and Raman techniques can be used to provide complementary information on the cellular environment. As fluorescence is a competing physical phenomenon to inelastic scattering, fluorescence background can be a significant issue in Raman spectroscopy, though this issue can be overcome with the use of near-infrared excitation and background subtraction [12]. Several approaches for multimodal fluorescence and Raman imaging have been investigated.…”

Section: Introductionmentioning

confidence: 99%

A Bimodal Fluorescence-Raman Probe for Cellular Imaging

Lin

Graziotto

Lay

et al. 2021

Cells

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Biochemical changes in specific organelles underpin cellular function, and studying these changes is crucial to understand health and disease. Fluorescent probes have become important biosensing and imaging tools as they can be targeted to specific organelles and can detect changes in their chemical environment. However, the sensing capacity of fluorescent probes is highly specific and is often limited to a single analyte of interest. A novel approach to imaging organelles is to combine fluorescent sensors with vibrational spectroscopic imaging techniques; the latter provides a comprehensive map of the relative biochemical distributions throughout the cell to gain a more complete picture of the biochemistry of organelles. We have developed NpCN1, a bimodal fluorescence-Raman probe targeted to the lipid droplets, incorporating a nitrile as a Raman tag. NpCN1 was successfully used to image lipid droplets in 3T3-L1 cells in both fluorescence and Raman modalities, reporting on the chemical composition and distribution of the lipid droplets in the cells.

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“…Raman in 1928. It represents a non-invasive optical method, which can be employed as a supplementary method in the diagnosis of certain diseases [1], and as an alternative to other more invasive methods, such as biopsy [2]. Moreover, Raman spectroscopy finds its application in various forms of research aiming at the development of biosensors [3,4].…”

Section: Introductionmentioning

confidence: 99%

Raman Spectroscopy-Based Quality Control of “Silicon-On-Insulator” Nanowire Chips for the Detection of Brain Cancer-Associated MicroRNA in Plasma

Malsagova

Popov

Kupriyanov

et al. 2021

Sensors

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Application of micro-Raman spectroscopy for the monitoring of quality of nanowire sensor chips fabrication has been demonstrated. Nanowire chips have been fabricated on the basis of «silicon-on-insulator» (SOI) structures (SOI-NW chips). The fabrication of SOI-NW chips was performed by optical litography with gas-phase etching. The so-fabricated SOI-NW chips are intended for highly sensitive detection of brain cancer biomarkers in humans. In our present study, two series of experiments have been conducted. In the first experimental series, detection of a synthetic DNA oligonucleotide (oDNA) analogue of brain cancer-associated microRNA miRNA-363 in purified buffer solution has been performed in order to demonstrate the high detection sensitivity. The second experimental series has been performed in order to reveal miRNA-363 itself in real human plasma samples. To provide detection biospecificity, the SOI-NW chip surface was modified by covalent immobilization of probe oligonucleotides (oDNA probes) complementary to the target biomolecules. Using the SOI-NW sensor chips proposed herein, the concentration detection limit of the target biomolecules at the level of 3.3 × 10−17 M has been demonstrated. Thus, the approach employing the SOI-NW chips proposed herein represents an attractive tool in biomedical practice, aimed at the early revelation of oncological diseases in humans.

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Raman Plus X: Biomedical Applications of Multimodal Raman Spectroscopy

Cited by 33 publications

References 133 publications

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

A Bimodal Fluorescence-Raman Probe for Cellular Imaging

Raman Spectroscopy-Based Quality Control of “Silicon-On-Insulator” Nanowire Chips for the Detection of Brain Cancer-Associated MicroRNA in Plasma

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